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Assessment of Open Biomass Burning Impacts on Surface PM2.5 Concentration
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摘要: 利用MODIS火点、土地类型、植被覆盖、生物质载荷和排放因子等数据产品，开发了露天生物质燃烧排放模型，并将其嵌入空气质量模式WRF-CUACE，通过敏感性试验定量评估了露天生物质燃烧对中国地面PM2.5浓度的影响。研究设计了3种模拟方案，比较模式评估结果发现修订后的方案能更好地模拟PM2.5浓度。结果表明：2014年10月露天生物质燃烧主要集中在我国东北、华南和西南地区，其对PM2.5月平均浓度的贡献达30~60 μg·m-3，局地甚至超过100 μg·m-3；华北、华东和华南地区生物质燃烧对PM2.5月平均浓度的贡献达5~20 μg·m-3。从相对贡献看，东北大部分地区生物质燃烧对地面PM2.5浓度的贡献超过50%，华南地区达20%~50%，西南局部地区甚至超过60%；华北、华中以及华东地区相对较低，平均相对贡献达10%~20%。生物质燃烧越严重的地区，其产生的PM2.5中二次气溶胶的贡献占比越小，反之亦然。Abstract: Open biomass burning plays an important role in the formation of heavy pollution events during harvest seasons in China by releasing gases and particulate matters into the atmosphere. A better understanding of open biomass burning in China is required to assess its impacts on the air quality and especially on heavy haze pollution.By using datasets of MODIS fire spot, land cover, vegetation cover, biomass loading and emission factors, a biomass emission model is developed, which is then embedded to an air quality model (WRF-CUACE) to quantitatively assess impacts of biomass burning on surface PM2.5 concentration in China through sensitivity tests. Three simulation scenarios are designed to ensure that simulation results of revised scenarios are closer to actual atmospheric conditions according to the model evaluation. Results show that in October 2014, Northeast, South and Southwest China are regions of the largest contribution to biomass burning with the average monthly increased concentration of PM2.5 up to 30-60 μg·m-3, and even more than 100 μg·m-3 at local regions. In North, East and South China, biomass burning generally provides a contribution of PM2.5 concentration of 5-20 μg·m-3. In terms of the percentage of relative contribution, the value in Northeast China exceeds 50% in most regions. In South China, the relative contribution of biomass burning reaches 20%-50%, and even exceeds 60% in parts of Southwest China. While in North, Central and East China, the relative contribution of biomass burning is generally 10%-20%. In addition, the contribution of secondary aerosols in PM2.5 from biomass burning is also estimated. A group of sensitivity experiments are set up, with and without the gas emission from biomass burning. In Northeast China, the contribution concentration of secondary aerosols is only 0-10 μg·m-3, significantly lower than that in North, Central, East and South China, where the contribution concentration of secondary aerosols could reach 5-15 μg·m-3. In terms of the percentage of contribution to secondary aerosols in PM2.5 from biomass burning, the value in Northeast China is the lowest, which is less than 30% in most regions. And in South and Southwest China, the contribution percentage is relatively larger, which can reach 30%-50%. While in North, Central, East China and vast remote areas, the contribution percentage almost exceed 70%. Based on the above analysis, it is found that the percentage of secondary aerosols in PM2.5 from biomass burning drops when the biomass burning grows.
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图 1 森林燃烧排放在垂直方向的分布
Fig. 1 Vertical distribution of biomass burning emissions from vegetation type of forest
图 2 模式的模拟区域(第2层)和所选城市站点的位置
Fig. 2 Model domain (the second one) and location of observation stations
图 3 2014年10月所选10个城市站点在3种模拟方案下观测和模拟的PM2.5日平均浓度
Fig. 3 The observed and simulated daily mean surface PM2.5 concentration of three scenarios at 10 stations during 1-31 Oct 2014
图 4 2014年10月地面PM2.5月平均浓度分布及生物质燃烧对PM2.5的贡献
(a)SIM1方案模拟的PM2.5浓度, (b)SIM3方案模拟的PM2.5浓度, (c)生物质燃烧对PM2.5的贡献值, (d)生物质燃烧对PM2.5的相对贡献百分比
Fig. 4 Distributions of averaged surface PM2.5 concentration and contribution from biomass burning in Oct 2014
(a)simulated PM2.5 concentration from SIM1, (b)simulated PM2.5 concentration from SIM3, (c)contribtution from biomass burning, (d)percentage of contribtution from biomass burning
图 5 生物质燃烧产生的PM2.5中二次气溶胶浓度的贡献值(a)和相对贡献百分比(b)
Fig. 5 Contribution(a) and percentage of contribution(b) of secondary aerosols in PM2.5 from biomass burning
表 1 不同植被覆盖类型对应的生物质载荷和排放因子
Table 1 Biomass loadings and emission factors for different land cover types
植被类型 木质类燃料载荷/
(kg·m-2)草本类燃料载荷/
(kg·m-2)CO/(g·kg-1) PM2.5/(g·kg-1) OC/(g·kg-1) BC/(g·kg-1) 常绿针叶林 28.61 4.79 118 13.0 7.8 0.20 常绿阔叶林 19.45 5.17 92 9.7 4.7 0.52 落叶针叶林 15.46 5.48 118 13.0 7.8 0.20 落叶阔叶林 19.50 4.73 102 13.0 9.2 0.56 混交林 19.98 7.93 102 13.0 9.2 0.56 稠密灌丛 4.80 1.24 68 9.3 6.6 0.50 稀疏灌丛 2.63 0.82 68 9.3 6.6 0.50 木本稀树草原 12.51 3.07 68 9.3 6.6 0.50 稀树草原 10.51 2.89 59 5.4 2.6 0.37 草地 2.62 1.40 59 5.4 2.6 0.37 永久湿地 8.34 10.14 59 5.4 2.6 0.37 农田 0 0.66 111 5.8 3.3 0.69 农田/自然植被混合 8.87 2.97 59 5.4 2.6 0.37 贫瘠或稀疏植被 1.18 0.48 59 5.4 2.6 0.37 
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表 2 气象要素的观测值与模拟值统计
Table 2 Statistics of observed and simulated meteorological elements
站点 要素 观测值 模拟值 平均偏差 均方根误差 相关系数 北京 气温/℃ 14.1 13.8 -0.3 2.1 0.90 相对湿度/% 64.0 56.8 -7.2 14.4 0.87 风速/(m·s-1) 1.7 1.9 0.2 0.9 0.70 济南 气温/℃ 17.1 17.9 0.7 1.6 0.94 相对湿度/% 56.9 48.2 -8.8 12.2 0.92 风速/(m·s-1) 2.8 2.9 0.1 1.2 0.70 石家庄 气温/℃ 15.4 15.8 0.5 2.8 0.82 相对湿度/% 68.9 52.2 -16.7 22.8 0.70 风速/(m·s-1) 0.9 2.0 1.0 1.5 0.38 合肥 气温/℃ 19.1 20.4 1.3 1.8 0.94 相对湿度/% 73.6 57.5 -16.1 19.0 0.82 风速/(m·s-1) 1.8 2.4 0.6 1.0 0.70 南京 气温/℃ 19.0 19.8 0.8 1.2 0.97 相对湿度/% 72.9 60.5 -12.4 14.7 0.90 风速/(m·s-1) 2.4 2.3 -0.1 0.9 0.80 上海 气温/℃ 20.2 20.7 0.5 1.1 0.95 相对湿度/% 68.3 64.3 -4.0 8.2 0.88 风速/(m·s-1) 2.6 3.2 0.6 1.2 0.79 郑州 气温/℃ 17.8 19.2 1.3 2.0 0.92 相对湿度/% 64.8 47.4 -17.4 20.7 0.85 风速/(m·s-1) 1.7 2.5 0.8 1.3 0.63 沈阳 气温/℃ 10.9 10.5 -0.4 2.3 0.94 相对湿度/% 55.7 59.8 4.0 14.0 0.83 风速/(m·s-1) 2.4 3.4 1.0 1.7 0.67 长春 气温/℃ 8.6 9.2 0.6 1.9 0.96 相对湿度/% 52.3 46.3 -6.0 14.8 0.77 风速/(m·s-1) 2.8 3.3 0.4 1.2 0.84 哈尔滨 气温/℃ 6.4 5.4 -0.9 2.1 0.95 相对湿度/% 55.3 52.8 -2.5 14.3 0.79 风速/(m·s-1) 2.7 3.4 0.7 1.4 0.81
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表 3 地面PM2.5浓度观测值和SIM1方案模拟值的统计量
Table 3 Statistics of PM2.5 between observation and simulation from SIM1
站点 平均值/(μg·m-3) 相关系数 平均偏差/
(μg·m-3)均方根误差/
(μg·m-3)平均相对偏差/% 平均相对误差/% 观测 模拟 北京 116.1 81.6 0.84 -34.5 59.3 -31.8 35.1 济南 71.6 53.4 0.46 -18.3 33.8 -27.4 37.7 石家庄 161.2 83.3 0.77 -77.8 104.8 -56.4 56.5 合肥 78.0 63.0 0.53 -15.0 35.0 -17.7 32.2 南京 69.7 52.8 0.79 -16.9 25.1 -26.5 31.2 上海 51.8 41.5 0.66 -19.9 21.9 -24.6 40.2 郑州 113.9 68.6 0.75 -45.4 53.8 -49.2 49.2 沈阳 88.1 63.1 0.66 -25.0 70.7 -14.3 40.4 长春 155.4 51.4 0.62 -104.0 135.5 -85.7 85.8 哈尔滨 147.6 33.7 0.55 -113.9 188.4 -83.0 83.0
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表 4 地面PM2.5浓度观测值和SIM2方案模拟值的统计量
Table 4 Statistics of PM2.5 between observation and simulation from SIM2
站点 平均值/(μg·m-3) 相关系数 平均偏差/
(μg·m-3)均方根误差/
(μg·m-3)平均相对偏差/% 平均相对误差/% 观测 模拟 北京 116.1 86.6 0.84 -29.4 55.3 -28.0 32.8 济南 71.6 55.4 0.45 -16.2 33.4 -24.0 37.1 石家庄 161.2 87.5 0.76 -73.7 101.6 -52.7 54.6 合肥 78.0 64.8 0.55 -13.2 33.9 -15.1 30.7 南京 69.7 55.4 0.84 -14.3 21.6 -22.7 27.9 上海 51.8 42.6 0.69 -17.8 20.7 -22.3 38.5 郑州 113.9 70.3 0.75 -38.3 52.4 -46.8 46.8 沈阳 88.1 69.5 0.69 -21.1 67.0 -5.1 36.8 长春 155.4 58.5 0.77 -97.0 126.9 -77.0 77.1 哈尔滨 147.6 39.5 0.73 -108.1 179.6 -76.2 76.3
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表 5 地面PM2.5浓度观测值和SIM3方案模拟值的统计量
Table 5 Statistics of PM2.5 between observation and simulation from SIM3
站点 平均值/(μg·m-3) 相关系数 平均偏差/
(μg·m-3)均方根误差/
(μg·m-3)平均相对偏差/% 平均相对误差/% 观测 模拟 北京 116.1 97.9 0.85 -18.2 48.3 -17.9 28.7 济南 71.6 60.5 0.41 -11.1 32.8 -15.9 36.7 石家庄 161.2 95.8 0.76 -65.4 94.0 -45.2 48.4 合肥 78.0 72.3 0.64 -5.7 29.2 -5.4 27.4 南京 69.7 61.3 0.85 -8.4 17.8 -13.8 22.8 上海 51.8 46.6 0.74 -5.2 18.6 -14.0 34.5 郑州 113.9 78.1 0.71 -35.8 47.4 -37.2 38.6 沈阳 88.1 93.4 0.57 5.3 67.5 21.3 40.1 长春 155.4 91.2 0.73 -64.2 96.2 -43.0 47.9 哈尔滨 147.6 71.1 0.79 -76.5 134.9 -45.8 50.7
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